Classifying Distributions via Symbolic Entropy Estimation

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dc.contributor.author Speidel, Ulrich en
dc.coverage.spatial Singapore en
dc.date.accessioned 2018-10-01T23:48:47Z en
dc.date.issued 2015-12-02 en
dc.identifier.uri http://hdl.handle.net/2292/38074 en
dc.description.abstract Shannon observed that the normal distribution has maximal entropy among distributions with a density function and a given variance. This sparked a significant body of research in statistics, broadly concerned with goodness-of-fit estimators based on Shannon entropy for a variety of distributions and, in particular, normality testing. The present paper proposes to use compression algorithms and other parsing-based entropy estimators to match samples in sampling order to one of a set of distributions with the observed and, where applicable, , using the distributions’ quantile functions to convert the samples into a string of symbols for entropy estimation. The paper demonstrates with a series of Monte-Carlo simulations that the proposed technique may be able to distinguish between a number of common distributions even if the samples themselves are not i.i.d. en
dc.relation.ispartof 10th International Conference on Information, Communications and Signal Processing en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Classifying Distributions via Symbolic Entropy Estimation en
dc.type Conference Item en
dc.rights.holder Copyright: The author en
pubs.author-url http://www.icics.org/2015/program/programSessionSchedule.asp?SessionID=We33 en
pubs.finish-date 2015-12-04 en
pubs.start-date 2015-12-02 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Proceedings en
pubs.elements-id 504642 en
pubs.org-id Faculty of Science en
pubs.org-id Computer Science en
pubs.record-created-at-source-date 2015-11-12 en


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